With the pervasive integration of Artificial Intelligence (AI) into various facets of society, concerns regarding its transparency and interpretability have gained prominence, particularly in critical or citizen-facing applications. The field of Explainable Artificial Intelligence (XAI) has witnessed rapid growth in response to these concerns, with recent research endeavors focusing on elucidating the inner workings of AI systems. We present a bibliometric study encompassing recent developments in XAI since 2020. We identify seven distinct areas of application where XAI methodologies have been applied. Furthermore, we propose a multidimensional taxonomy that categorizes these approaches and applications, aiming to contribute to the ongoing efforts towards the standardization and uniformization of XAI practices. By shedding light on the current landscape of research and offering a structured taxonomy for analysis, we expose under and over-explored techniques, and encourage the employment and development of diversified approaches for XAI.